A case-first reading of Conversational Demand Response, where AI agents do not replace energy optimization but make household flexibility negotiable, explainable, and operationally usable.
A mechanism-first reading of SAHOO, a framework for monitoring drift, preserving constraints, and deciding when recursive AI self-improvement should stop.
A business-readable interpretation of schema-gated orchestration: why agentic AI should keep conversation flexible but execution formally constrained.
A mechanism-first reading of H2RL, a neuro-symbolic reinforcement learning framework that uses logic as training scaffolding rather than inference-time baggage.
A case-first reading of how reinforcement learning can turn long-term flood adaptation from a fixed infrastructure plan into a staged, testable capital-allocation strategy.
EpisTwin shows why serious personal AI may need explicit knowledge graphs, not just longer context windows or better vector search.
A mechanism-first look at how pose-based shoplifting detection moves from static video anomaly benchmarks toward periodically adapting retail IoT systems.
A mechanism-first reading of Interactive Benchmarks, showing why the next useful AI evaluation may measure how models acquire information, not just how confidently they answer.
CONE shows why numerical AI failures are often embedding failures: numbers need magnitude, units, and attribute context before retrieval or reasoning can become reliable.
A mechanism-first reading of how Gemini Deep Think, Tree Search, executable verification, and human review turned a difficult cosmic-string integral into a case study for credible AI-assisted discovery.